A Resource-Efficient Feature Extraction Framework for Image Processing in IoT Devices

Extracting features from image data on Internet of Things (IoT) devices to reduce the amount of data that needs to be uploaded to cloud/edge servers has received increasing attention. However, most of the existing related approaches suffer from two major limitations, (i) low performance and high network traffic, and (ii) a lot of storage resource consumption. To this end, we propose a resource-efficient feature extraction framework for image processing in IoT devices. The proposed framework consists of the edge-assisted extractor generation method and the NestE method. The extractor generated by the edge-assisted extractor generation method can extract the features required by the application, which can not only avoid the IoT device uploading useless feature data but also improve application performance. The proposed NestE generates a nonredundant subextractor by splitting the extractor into multiple subextractors, removing redundant subextractors, and nesting small-capacity subextractors in large-capacity subextractors in a parameter-sharing manner. Compared with deploying multiple independent subextractors on IoT devices, deploying the nonredundant multifunctional extractor can save considerable storage resources and switching overhead. Extensive experimental results show that the proposed framework reduces the storage footprint by approximately 90.7% and switching overhead by approximately 92.4% compared with deploying independent subextractors when using the classical principal component analysis algorithm.